Single-channel mixed speech recognition using deep neural networks

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning: Methods and Applications (2014, background): “to emotion recognition from speech in [207, 222], to spoken language understanding in [242, 366, 403], to speaker recognition in [351, 372], to language-type recognition in [112], to dialogue state tracking for spoken dialogue systems in [94, 152], to automatic voice activity detection in [442], to ”
Toward Human Parity in Conversational Speech Recognition (2017, background): “carefully read newspaper speech [3] and rigidly constrained small-vocabulary tasks in noise [4], [5].”
Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition (2015, methodology): “The best setup of our proposed systems achieves an average word error rate (WER) of 18.8%across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.”